Department of Ultrasound Imaging, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China.
Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China.
Med Mol Morphol. 2024 Dec;57(4):286-298. doi: 10.1007/s00795-024-00399-8. Epub 2024 Aug 1.
The aim of this study is to establish a deep learning (DL) model to predict the pathological type of gastric adenocarcinoma cancer based on whole-slide images(WSIs). We downloaded 356 histopathological images of gastric adenocarcinoma (STAD) patients from The Cancer Genome Atlas database and randomly divided them into the training set, validation set and test set (8:1:1). Additionally, 80 H&E-stained WSIs of STAD were collected for external validation. The CLAM tool was used to cut the WSIs and further construct the model by DL algorithm, achieving an accuracy of over 90% in identifying and predicting histopathological subtypes. External validation results demonstrated the model had a certain generalization ability. Moreover, DL features were extracted from the model to further investigate the differences in immune infiltration and patient prognosis between the two subtypes. The DL model can accurately predict the pathological classification of STAD patients, and provide certain reference value for clinical diagnosis. The nomogram combining DL-signature, gene-signature and clinical features can be used as a prognostic classifier for clinical decision-making and treatment.
本研究旨在建立一种基于全切片图像（WSI）的深度学习（DL）模型，以预测胃腺癌（STAD）的病理类型。我们从癌症基因组图谱（TCGA）数据库中下载了 356 例胃腺癌患者的 356 张组织病理学图像，并将其随机分为训练集、验证集和测试集（8:1:1）。此外，还收集了 80 例 STAD 的 H&E 染色 WSI 用于外部验证。使用 CLAM 工具切割 WSI，并进一步通过 DL 算法构建模型，在识别和预测组织病理学亚型方面的准确率超过 90%。外部验证结果表明该模型具有一定的泛化能力。此外，还从模型中提取了 DL 特征，以进一步研究两种亚型之间的免疫浸润和患者预后的差异。该 DL 模型可以准确预测 STAD 患者的病理分类，为临床诊断提供一定的参考价值。结合 DL 特征、基因特征和临床特征的列线图可作为临床决策和治疗的预后分类器。
